2.4 SFARI Genes
Load R packages
library(tidyverse) ; library(reshape2) ; library(glue) ; library(plotly)
library(RColorBrewer) ; library(viridis) ; require(gridExtra) ; library(GGally) ; library(ggpubr)
library(rstatix)
library(expss)
library(DESeq2)
library(knitr) ; library(kableExtra)Load preprocessed dataset (code in 2.1.Preprocessing_pipeline.Rmd)
# SFARI Genes
SFARI_genes = read_csv('./../../SFARI/Data/SFARI_genes_01-03-2020_w_ensembl_IDs.csv')
# Load Wright dataset
load('./../Data/preprocessedData/preprocessed_data.RData')
datExpr = datExpr %>% data.frame
# Updates genes_info with SFARI information
genes_info = genes_info %>% left_join(SFARI_genes, by = 'ID') %>%
mutate(gene.score = ifelse(is.na(`gene-score`) & Neuronal==0, 'Others',
ifelse(is.na(`gene-score`), 'Neuronal', `gene-score`))) %>%
mutate(Group = factor(ifelse(gene.score %in% c('Neuronal','Others'), gene.score, 'SFARI'),
levels = c('SFARI', 'Neuronal', 'Others')))
# SFARI palette
SFARI_colour_hue = function(r) {
pal = c('#FF7631','#FFB100','#E8E328','#8CC83F','#62CCA6','#59B9C9','#b3b3b3','#808080','gray','#d9d9d9')[r]
}There are 783 SFARI Genes in the expression dataset (~70%), 714 of them have a SFARI Gene Score.
Gene count by SFARI score:
table_info = genes_info %>% apply_labels(`gene-score` = 'SFARI Gene Score', syndromic = 'Syndromic Tag',
Neuronal = 'Neuronal Function', gene.score = 'Gene Score') %>%
mutate(syndromic = as.logical(syndromic), Neuronal = as.logical(Neuronal))
cro(table_info$`gene-score`)|  #Total | |
|---|---|
|  SFARI Gene Score | |
| Â Â Â 1Â | 114 |
| Â Â Â 2Â | 188 |
| Â Â Â 3Â | 412 |
|    #Total cases | 714 |
Gene count by Syndromic tag:
cro(table_info$syndromic)|  #Total | |
|---|---|
|  Syndromic Tag | |
| Â Â Â FALSEÂ | 678 |
| Â Â Â TRUEÂ | 105 |
|    #Total cases | 783 |
Neuronal annotations:
1477 genes have neuronal-related annotations, 190 of these, have a SFARI score
cro(table_info$gene.score[genes_info$`gene-score` %in% as.character(c(1:3))],
list(table_info$Neuronal[genes_info$`gene-score` %in% as.character(c(1:3))], total()))|  Neuronal Function |  #Total | |||
|---|---|---|---|---|
| Â FALSEÂ | Â TRUEÂ | Â | ||
|  Gene Score | ||||
| Â Â Â 1Â | 73 | 41 | Â | 114 |
| Â Â Â 2Â | 131 | 57 | Â | 188 |
| Â Â Â 3Â | 320 | 92 | Â | 412 |
|    #Total cases | 524 | 190 |  | 714 |
rm(table_info)2.4.1 Analysis of all SFARI Genes together
Mean Expression
plot_data = data.frame('ID'=rownames(datExpr), 'MeanExpr'=rowMeans(datExpr)) %>%
left_join(genes_info, by='ID')
wt = plot_data %>% wilcox_test(MeanExpr~Group, p.adjust.method='BH') %>% add_x_position(x = 'group')
increase = 1.3
base = 13
pos_y_comparisons = c(base, base+increase, base)
plot_data %>% ggplot(aes(Group, MeanExpr)) +
geom_boxplot(outlier.colour='#cccccc', outlier.shape='o', outlier.size=3, aes(fill=Group)) +
stat_pvalue_manual(wt, y.position = pos_y_comparisons, tip.length = .03) +
scale_fill_manual(values=c('#00A4F7', SFARI_colour_hue(r=c(8,7)))) +
xlab('') + ylab('Mean Expression') + #ggtitle('Mean Expression Comparison') +
scale_x_discrete(labels=c('SFARI' = 'SFARI\nGenes', 'Others' = 'Other\ngenes',
'Neuronal' = 'Neuronal\ngenes')) +
theme_minimal() + theme(legend.position='none')
Log Fold Change
The propotion of over and underexpressed genes is very simiar for each group, so we can just focus on the magnitude of the LFC independently of the sign.
genes_info %>% mutate(direction = ifelse(log2FoldChange>0, 'overexpressed', 'underexpressed')) %>%
group_by(Group, direction) %>% tally(name = 'overexpressed') %>%
filter(direction == 'overexpressed') %>% ungroup %>%
left_join(genes_info %>% group_by(Group) %>% tally(name = 'Total'), by = 'Group') %>% ungroup %>%
mutate('underexpressed' = Total - overexpressed ,
'ratio' = round(overexpressed/underexpressed,2)) %>%
dplyr::select(Group, overexpressed, underexpressed, ratio) %>%
kable %>% kable_styling(full_width = F)| Group | overexpressed | underexpressed | ratio |
|---|---|---|---|
| SFARI | 362 | 352 | 1.03 |
| Neuronal | 569 | 718 | 0.79 |
| Others | 6685 | 6840 | 0.98 |
plot_data = data.frame('ID'=rownames(datExpr), 'MeanExpr'=rowMeans(datExpr)) %>%
left_join(genes_info, by='ID')
wt = plot_data %>% filter(log2FoldChange>0) %>%
wilcox_test(MeanExpr~Group, p.adjust.method='BH') %>% add_x_position(x = 'group')
increase = 1.3
base = 13
pos_y_comparisons = c(base, base+increase, base)
p1 = plot_data %>% filter(log2FoldChange>0) %>% ggplot(aes(Group, MeanExpr)) +
geom_boxplot(outlier.colour='#cccccc', outlier.shape='o', outlier.size=3, aes(fill=Group)) +
stat_pvalue_manual(wt, y.position = pos_y_comparisons, tip.length = .03) +
scale_fill_manual(values=c('#00A4F7', SFARI_colour_hue(r=c(8,7)))) +
xlab('') + ylab('Mean Expression') + ggtitle('Overexpressed genes') +
scale_x_discrete(labels=c('SFARI' = 'SFARI\nGenes', 'Others' = 'Other\ngenes',
'Neuronal' = 'Neuronal\ngenes')) +
theme_minimal() + theme(legend.position='none')
wt = plot_data %>% filter(log2FoldChange<0) %>%
wilcox_test(MeanExpr~Group, p.adjust.method='BH') %>% add_x_position(x = 'group')
p2 = plot_data %>% filter(log2FoldChange<0) %>% ggplot(aes(Group, MeanExpr)) +
geom_boxplot(outlier.colour='#cccccc', outlier.shape='o', outlier.size=3, aes(fill=Group)) +
stat_pvalue_manual(wt, y.position = pos_y_comparisons, tip.length = .03) +
scale_fill_manual(values=c('#00A4F7', SFARI_colour_hue(r=c(8,7)))) +
xlab('') + ylab('Mean Expression') + ggtitle('Underexpressed genes') +
scale_x_discrete(labels=c('SFARI' = 'SFARI\nGenes', 'Others' = 'Other\ngenes',
'Neuronal' = 'Neuronal\ngenes')) +
theme_minimal() + theme(legend.position='none')
grid.arrange(p1, p2, nrow=1)
Both with the original LFC values as well as the shrunken ones, the difference in LFC magnitude between SFARI genes and non-SFARI, non-Neuronal genes is statisically significant with a p-value lower than \(10^{-3}\), but the difference between SFARI and Neuronal genes is only statistically significant when comparing the original LFC magnitues.
wt = genes_info %>% mutate(abs_lfc = abs(log2FoldChange),
Group = factor(Group, levels = c('SFARI','Others', 'Neuronal'))) %>%
wilcox_test(abs_lfc~Group, p.adjust.method='BH') %>% add_x_position(x = 'group')
increase = 0.02
base = 0.32
pos_y_comparisons = c(base, base + increase, base)
p1 = genes_info %>% mutate(Group = factor(Group, levels = c('SFARI','Others', 'Neuronal'))) %>%
ggplot(aes(Group, abs(log2FoldChange))) +
geom_boxplot(outlier.colour='#cccccc', outlier.shape='o', outlier.size=3, aes(fill=Group)) +
stat_pvalue_manual(wt, y.position = pos_y_comparisons, tip.length = .003) +
scale_fill_manual(values=c('#00A4F7', SFARI_colour_hue(r=c(7,8)))) +
coord_cartesian(ylim= c(0, max(pos_y_comparisons))) +
scale_x_discrete(labels=c('SFARI' = 'SFARI\nGenes', 'Others' = 'Other\ngenes',
'Neuronal' = 'Neuronal\ngenes')) +
xlab('') + ylab('Original LFC Magnitude') + theme_minimal() + theme(legend.position='none')
wt = genes_info %>% mutate(abs_lfc = abs(shrunken_log2FoldChange),
Group = factor(Group, levels = c('SFARI','Others', 'Neuronal'))) %>%
wilcox_test(abs_lfc~Group, p.adjust.method='BH') %>% add_x_position(x = 'group')
increase = 0.003
base = 0.065
pos_y_comparisons = c(base, base + increase, base)
p2 = genes_info %>% mutate(Group = factor(Group, levels = c('SFARI','Others', 'Neuronal'))) %>%
ggplot(aes(Group, abs(shrunken_log2FoldChange))) +
geom_boxplot(outlier.colour='#cccccc', outlier.shape='o', outlier.size=3, aes(fill=Group)) +
stat_pvalue_manual(wt, y.position = pos_y_comparisons, tip.length = .01) +
scale_fill_manual(values=c('#00A4F7', SFARI_colour_hue(r=c(7,8)))) +
coord_cartesian(ylim= c(0, max(pos_y_comparisons))) +
scale_x_discrete(labels=c('SFARI' = 'SFARI\nGenes', 'Others' = 'Other\ngenes',
'Neuronal' = 'Neuronal\ngenes')) +
xlab('') + ylab('Shrunken LFC Magnitude') + theme_minimal() + theme(legend.position='none')
grid.arrange(p1, p2, nrow = 1)
rm(increase, base, pos_y_comparisons, wt)Full plots, without cropping out outliers
p1 = ggplotly(genes_info %>% mutate(Group = factor(Group, levels = c('SFARI','Others', 'Neuronal'))) %>%
ggplot(aes(Group, abs(log2FoldChange))) +
geom_boxplot(outlier.colour='#cccccc', outlier.shape='o', outlier.size=3, aes(fill=Group)) +
scale_fill_manual(values=c('#00A4F7', SFARI_colour_hue(r=c(7,8)))) +
scale_x_discrete(labels=c('SFARI' = 'SFARI\nGenes', 'Others' = 'Other\ngenes',
'Neuronal' = 'Neuronal\ngenes')) +
xlab('') + ylab('Original LFC Magnitude') + theme_minimal() + theme(legend.position='none'))
p2 = ggplotly(genes_info %>% mutate(Group = factor(Group, levels = c('SFARI','Others', 'Neuronal'))) %>%
ggplot(aes(Group, abs(shrunken_log2FoldChange))) +
geom_boxplot(outlier.colour='#cccccc', outlier.shape='o', outlier.size=3, aes(fill=Group)) +
scale_fill_manual(values=c('#00A4F7', SFARI_colour_hue(r=c(7,8)))) +
scale_x_discrete(labels=c('SFARI' = 'SFARI\nGenes', 'Others' = 'Other\ngenes',
'Neuronal' = 'Neuronal\ngenes')) +
xlab('') + ylab('Shrunken LFC Magnitude') + theme_minimal() + theme(legend.position='none'))
subplot(p1, p2, nrows = 1)Differential Expression
There are no DE genes in the SFARI group
genes_info %>% group_by(Group, significant) %>% tally(name = 'DE') %>% filter(significant) %>% ungroup %>%
left_join(genes_info %>% group_by(Group) %>% tally(name = 'Total'), by = 'Group') %>% ungroup %>%
mutate('prop_DE' = round(DE/Total,2)) %>% dplyr::select(Group, DE, prop_DE, Total) %>%
kable(caption = 'Proportion of DE Genes by Group') %>% kable_styling(full_width = F)| Group | DE | prop_DE | Total |
|---|---|---|---|
| Others | 3 | 0 | 13525 |
2.4.2 Analysis of SFARI Genes by SFARI Score
Mean Expression
plot_data = data.frame('ID'=rownames(datExpr), 'MeanExpr'=rowMeans(datExpr)) %>% left_join(genes_info, by='ID')
wt = plot_data %>% wilcox_test(MeanExpr~gene.score, p.adjust.method='BH') %>% add_x_position(x = 'group')
increase = 1.3
base = 12.5
pos_y_comparisons = c(base + c(0,1,3,5)*increase, base+c(0,2,4)*increase, base+0:1*increase, base)
plot_data %>% ggplot(aes(gene.score, MeanExpr)) +
geom_boxplot(outlier.colour='#cccccc', outlier.shape='o', outlier.size=3, aes(fill=gene.score)) +
stat_pvalue_manual(wt, y.position = pos_y_comparisons, tip.length = .02) +
scale_fill_manual(values=SFARI_colour_hue(r=c(1:3,8,7))) +
xlab('') + ylab('Mean Expression') +
scale_x_discrete(labels=c('1' = 'SFARI\nScore 1', '2' = 'SFARI\nScore 2', '3' = 'SFARI\nScore 3',
'Others' = 'Other\ngenes', 'Neuronal' = 'Neuronal\ngenes')) +
theme_minimal() + theme(legend.position='none')
Log Fold Change
genes_info %>% mutate(direction = ifelse(log2FoldChange>0, 'overexpressed', 'underexpressed')) %>%
group_by(gene.score, direction) %>% tally(name = 'overexpressed') %>%
filter(direction == 'overexpressed') %>% ungroup %>%
left_join(genes_info %>% group_by(gene.score) %>% tally(name = 'Total'), by = 'gene.score') %>% ungroup %>%
mutate('underexpressed' = Total - overexpressed ,
'ratio' = round(overexpressed/underexpressed,2)) %>%
dplyr::select(gene.score, overexpressed, underexpressed, ratio) %>%
kable %>% kable_styling(full_width = F)| gene.score | overexpressed | underexpressed | ratio |
|---|---|---|---|
| 1 | 57 | 57 | 1.00 |
| 2 | 97 | 91 | 1.07 |
| 3 | 208 | 204 | 1.02 |
| Neuronal | 569 | 718 | 0.79 |
| Others | 6685 | 6840 | 0.98 |
plot_data = data.frame('ID'=rownames(datExpr), 'MeanExpr'=rowMeans(datExpr)) %>%
left_join(genes_info, by='ID')
wt = plot_data %>% filter(log2FoldChange>0) %>%
wilcox_test(MeanExpr~gene.score, p.adjust.method='BH') %>% add_x_position(x = 'group')
increase = 1.5
base = 13
pos_y_comparisons = c(base + c(0,1,3,5)*increase, base+c(0,2,4)*increase, base+0:1*increase, base)
p1 = plot_data %>% filter(log2FoldChange>0) %>% ggplot(aes(gene.score, MeanExpr)) +
geom_boxplot(outlier.colour='#cccccc', outlier.shape='o', outlier.size=3, aes(fill=gene.score)) +
stat_pvalue_manual(wt, y.position = pos_y_comparisons, tip.length = .025) +
scale_fill_manual(values=SFARI_colour_hue(r=c(1:3,8,7))) +
xlab('') + ylab('Mean Expression') + ggtitle('Overexpressed genes') +
scale_x_discrete(labels=c('1' = 'SFARI\nScore 1', '2' = 'SFARI\nScore 2', '3' = 'SFARI\nScore 3',
'Others' = 'Other\ngenes', 'Neuronal' = 'Neuronal\ngenes')) +
theme_minimal() + theme(legend.position='none')
wt = plot_data %>% filter(log2FoldChange<0) %>%
wilcox_test(MeanExpr~gene.score, p.adjust.method='BH') %>% add_x_position(x = 'group')
p2 = plot_data %>% filter(log2FoldChange<0) %>% ggplot(aes(gene.score, MeanExpr)) +
geom_boxplot(outlier.colour='#cccccc', outlier.shape='o', outlier.size=3, aes(fill=gene.score)) +
stat_pvalue_manual(wt, y.position = pos_y_comparisons, tip.length = .025) +
scale_fill_manual(values=SFARI_colour_hue(r=c(1:3,8,7))) +
xlab('') + ylab('Mean Expression') + ggtitle('Underexpressed genes') +
scale_x_discrete(labels=c('1' = 'SFARI\nScore 1', '2' = 'SFARI\nScore 2', '3' = 'SFARI\nScore 3',
'Others' = 'Other\ngenes', 'Neuronal' = 'Neuronal\ngenes')) +
theme_minimal() + theme(legend.position='none')
grid.arrange(p1, p2, nrow=1)
Both with the original LFC values as well as the shrunken ones, the difference in LFC magnitude between SFARI genes and Neuronal genes is statisically significant with a p-value lower than \(10^{-4}\).
wt = genes_info %>% mutate(abs_lfc = abs(log2FoldChange),
Group = factor(gene.score, levels = c('1','2','3','Others','Neuronal'))) %>%
wilcox_test(abs_lfc~Group, p.adjust.method='BH') %>% add_x_position(x = 'group')
increase = 0.04
base = 0.35
pos_y_comparisons = c(base + c(0,1,3,5)*increase, base+c(0,2,4)*increase, base+0:1*increase, base)
p1 = genes_info %>% mutate(gene.score = factor(gene.score, levels = c('1','2','3','Others','Neuronal'))) %>%
ggplot(aes(gene.score, abs(log2FoldChange))) +
geom_boxplot(outlier.colour='#cccccc', outlier.shape='o', outlier.size=3, aes(fill=gene.score)) +
stat_pvalue_manual(wt, y.position = pos_y_comparisons, tip.length = .005) +
scale_fill_manual(values=SFARI_colour_hue(r=c(1:3,7,8))) +
coord_cartesian(ylim= c(0, max(pos_y_comparisons))) +
scale_x_discrete(labels=c('1' = 'SFARI\nScore 1', '2' = 'SFARI\nScore 2', '3' = 'SFARI\nScore 3',
'Neuronal' = 'Neuronal\ngenes','Others' = 'Other\ngenes')) +
xlab('') + ylab('Original LFC Magnitude') + theme_minimal() + theme(legend.position='none')
wt = genes_info %>% mutate(abs_lfc = abs(shrunken_log2FoldChange),
Group = factor(gene.score, levels = c('1','2','3','Others', 'Neuronal'))) %>%
wilcox_test(abs_lfc~Group, p.adjust.method='BH') %>% add_x_position(x = 'group')
increase = 0.005
base = 0.065
pos_y_comparisons = c(base + c(0,1,3,5)*increase, base+c(0,2,4)*increase, base+0:1*increase, base)
p2 = genes_info %>% mutate(gene.score = factor(gene.score, levels = c('1','2','3','Others','Neuronal'))) %>%
ggplot(aes(gene.score, abs(shrunken_log2FoldChange))) +
geom_boxplot(outlier.colour='#cccccc', outlier.shape='o', outlier.size=3, aes(fill=gene.score)) +
stat_pvalue_manual(wt, y.position = pos_y_comparisons, tip.length = .015) +
scale_fill_manual(values=SFARI_colour_hue(r=c(1:3,7,8))) +
coord_cartesian(ylim= c(0, max(pos_y_comparisons))) +
scale_x_discrete(labels=c('1' = 'SFARI\nScore 1', '2' = 'SFARI\nScore 2', '3' = 'SFARI\nScore 3',
'Neuronal' = 'Neuronal\ngenes', 'Others' = 'Other\ngenes')) +
xlab('') + ylab('Shrunken LFC Magnitude') + theme_minimal() + theme(legend.position='none')
grid.arrange(p1, p2, nrow = 1)
rm(increase, base, pos_y_comparisons, wt)Full plots, without cropping out outliers
p1 = genes_info %>% mutate(gene.score = factor(gene.score, levels = c('1','2','3','Others','Neuronal'))) %>%
ggplot(aes(gene.score, abs(log2FoldChange))) +
geom_boxplot(outlier.colour='#cccccc', outlier.shape='o', outlier.size=3, aes(fill=gene.score)) +
scale_fill_manual(values=SFARI_colour_hue(r=c(1:3,7,8))) +
scale_x_discrete(labels=c('1' = 'SFARI\nScore 1', '2' = 'SFARI\nScore 2', '3' = 'SFARI\nScore 3',
'Neuronal' = 'Neuronal\ngenes','Others' = 'Other\ngenes')) +
xlab('') + ylab('Original LFC Magnitude') + theme_minimal() + theme(legend.position='none')
p2 = genes_info %>% mutate(gene.score = factor(gene.score, levels = c('1','2','3','Others','Neuronal'))) %>%
ggplot(aes(gene.score, abs(shrunken_log2FoldChange))) +
geom_boxplot(outlier.colour='#cccccc', outlier.shape='o', outlier.size=3, aes(fill=gene.score)) +
scale_fill_manual(values=SFARI_colour_hue(r=c(1:3,7,8))) +
scale_x_discrete(labels=c('1' = 'SFARI\nScore 1', '2' = 'SFARI\nScore 2', '3' = 'SFARI\nScore 3',
'Neuronal' = 'Neuronal\ngenes', 'Others' = 'Other\ngenes')) +
xlab('') + ylab('Shrunken LFC Magnitude') + theme_minimal() + theme(legend.position='none')
subplot(ggplotly(p1), ggplotly(p2), nrows=1)Differential Expression
There are no DE genes in the SFARI Gene group
Session info
sessionInfo()## R version 3.6.3 (2020-02-29)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.5 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.7.1
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.7.1
##
## locale:
## [1] LC_CTYPE=en_GB.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_GB.UTF-8 LC_COLLATE=en_GB.UTF-8
## [5] LC_MONETARY=en_GB.UTF-8 LC_MESSAGES=en_GB.UTF-8
## [7] LC_PAPER=en_GB.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] kableExtra_1.1.0 knitr_1.32
## [3] DESeq2_1.24.0 SummarizedExperiment_1.14.1
## [5] DelayedArray_0.10.0 BiocParallel_1.18.1
## [7] matrixStats_0.56.0 Biobase_2.44.0
## [9] GenomicRanges_1.36.1 GenomeInfoDb_1.20.0
## [11] IRanges_2.18.3 S4Vectors_0.22.1
## [13] BiocGenerics_0.30.0 expss_0.10.2
## [15] rstatix_0.6.0 ggpubr_0.2.5
## [17] magrittr_2.0.1 GGally_1.5.0
## [19] gridExtra_2.3 viridis_0.5.1
## [21] viridisLite_0.4.0 RColorBrewer_1.1-2
## [23] plotly_4.9.2 glue_1.4.2
## [25] reshape2_1.4.4 forcats_0.5.0
## [27] stringr_1.4.0 dplyr_1.0.1
## [29] purrr_0.3.4 readr_1.3.1
## [31] tidyr_1.1.0 tibble_3.1.2
## [33] ggplot2_3.3.5 tidyverse_1.3.0
##
## loaded via a namespace (and not attached):
## [1] readxl_1.3.1 backports_1.1.8 Hmisc_4.4-0
## [4] plyr_1.8.6 lazyeval_0.2.2 splines_3.6.3
## [7] crosstalk_1.1.0.1 digest_0.6.27 htmltools_0.5.1.1
## [10] fansi_0.5.0 checkmate_2.0.0 memoise_1.1.0
## [13] cluster_2.1.0 openxlsx_4.1.4 annotate_1.62.0
## [16] modelr_0.1.6 jpeg_0.1-8.1 colorspace_2.0-2
## [19] blob_1.2.1 rvest_0.3.5 haven_2.2.0
## [22] xfun_0.22 crayon_1.4.1 RCurl_1.98-1.2
## [25] jsonlite_1.7.2 genefilter_1.66.0 survival_3.2-7
## [28] gtable_0.3.0 zlibbioc_1.30.0 XVector_0.24.0
## [31] webshot_0.5.2 car_3.0-7 abind_1.4-5
## [34] scales_1.1.1 DBI_1.1.0 Rcpp_1.0.6
## [37] xtable_1.8-4 htmlTable_1.13.3 foreign_0.8-76
## [40] bit_4.0.4 Formula_1.2-3 htmlwidgets_1.5.1
## [43] httr_1.4.2 acepack_1.4.1 ellipsis_0.3.2
## [46] pkgconfig_2.0.3 reshape_0.8.8 XML_3.99-0.3
## [49] farver_2.1.0 nnet_7.3-14 sass_0.4.0
## [52] dbplyr_1.4.2 locfit_1.5-9.4 utf8_1.2.1
## [55] tidyselect_1.1.0 labeling_0.4.2 rlang_0.4.11
## [58] AnnotationDbi_1.46.1 munsell_0.5.0 cellranger_1.1.0
## [61] tools_3.6.3 cli_2.5.0 generics_0.0.2
## [64] RSQLite_2.2.0 broom_0.7.0 evaluate_0.14
## [67] yaml_2.2.1 bit64_4.0.5 fs_1.5.0
## [70] zip_2.0.4 xml2_1.2.5 compiler_3.6.3
## [73] rstudioapi_0.11 curl_4.3 png_0.1-7
## [76] ggsignif_0.6.0 reprex_0.3.0 geneplotter_1.62.0
## [79] bslib_0.2.5.1 stringi_1.5.3 highr_0.8
## [82] lattice_0.20-41 Matrix_1.2-18 vctrs_0.3.8
## [85] pillar_1.6.1 lifecycle_1.0.0 jquerylib_0.1.4
## [88] data.table_1.12.8 bitops_1.0-6 R6_2.5.0
## [91] latticeExtra_0.6-29 rio_0.5.16 assertthat_0.2.1
## [94] withr_2.4.2 GenomeInfoDbData_1.2.1 hms_0.5.3
## [97] grid_3.6.3 rpart_4.1-15 rmarkdown_2.7
## [100] carData_3.0-3 lubridate_1.7.4 base64enc_0.1-3